Architect
Founding Member of Technical Staff - ML Infra
Palo Alto, CA · Staff+
Sponsorship not specifiedDetected 16 days ago
AlgorithmsMachine LearningLLMsResearch
About the role
- Born out of Stanford, our team blends researchers and engineers from Anthropic, Google DeepMind, NVIDIA, Meta SuperIntelligence Labs, Apple, and Intel.
Responsibilities
- Your work will directly enable breakthroughs in AI capabilities in chip designs.
- Collaborate closely with ML researchers to implement stable and fast versions of new finetuning recipes (like in RLHF/SFT) on different model architectures.
- ML Infrastructure: Proven track record of building end-to-end ML pipelines, including data curation, preparation, and large-scale LLM finetuning (RLHF, SFT).
- About Architect Architect is a frontier AI research and product lab for chip design.
- We build AI models and systems that can explore, design, optimize, and verify new hardware.
- Our goal is to reimagine chip design using AI, cut down ASIC design time and cost, and enable a new era of ultra-efficient, domain-specific chips powering the future of computation.
- Backed by leading VCs and angels, including the Chief Scientist at Google, Stanford professors, and founders of chip companies, Architect currently operates in stealth, pushing the limits of AI4EDA and building the intelligence layer for the hardware revolution.
Requirements
- What We'd Like to See Qualifications & Skills: Degree: PhD in Computer Science, EECS, Mathematics, or a closely related field.
- Experience with implementing LLM finetuning algorithms (such as RLHF) and modifying systems based on model architectures.
Nice to have
- Foundation in Electrical/Computer Engineering and chip-design or verification processes (not required, but a plus).
Skills
- Degree: PhD in Computer Science, EECS, Mathematics, or a closely related field.
- Preferably, specialization in Machine Learning, Systems, or Artificial Intelligence.
- Or BS/MS with a strong research engineering background from frontier labs.
- Execution: Results-oriented with a bias towards flexibility and impact.
- Bonus: Experience with implementing LLM finetuning algorithms (such as RLHF) and modifying systems based on model architectures.
- Publications in top ML (NeurIPS, ICLR, ICML) or Systems (OSDI, SOSP) venues.
Benefits
- Competitive salary and meaningful equity stake Fast-paced startup with autonomy and visible impact Cutting-edge AI-driven chip design challenges
- Adept at diagnosing why training runs slow down, building instrumentation to monitor system health, and fixing complex issues in distributed environments.
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